Decision tree learning with random forest models using agricultural and ecological field data incorporating multi-factor studies and covariate structure
Bibliographic record
Abstract
Abstract In this study, the decision learning methods of regression tree and random forest analysis are investigated as complements to standard statistical methods such as analysis of variance and grouped regression. For this purpose, three diverse data sets were used. The first set is large and multidimensional and describes nitrous oxide emissions from sites across different geo-positions in the UK receiving various fertilisation treatments. The second set is based on Gliricidia tree provenances and has a small number of samples and an imbalanced distribution of factor classes. Random forest modelling was found to be a very viable option in the case of the first data set but failed in the case of second. The third data set, based on count observations recording osprey egg incubation times, lends itself to tree and forest modelling. These decision learning methods therefore appear well suited to handling the diverse, multi-dimensional and complex data sets that often arise in carrying out agricultural and ecological field experiments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".